RoPE: Understanding Rotary Positional Embeddings in transformers

Mastering Rotary Positional Embeddings (RoPE): From Zero to Deep Dive Unlock the secrets behind modern Large Language Model (LLM) architectures in this comprehensive breakdown of Rotary Positional Embeddings (RoPE). Sparked by the introduction of "pruned RoPE" in Gemma 4, this video provides a complete "brain dump" on how models maintain token order and spatial context. Chapter Timestamp: 00:00 - Introduction to RoPE 00:40 - The Need for Positional Embeddings 04:51 - Integer and Binary Positional Embeddings 06:45 - Sinusoidal Positional Embeddings 08:15 - Multiplicative Intuition and Rotation 10:58 - Deep Dive into Rotary Positional Embeddings (RoPE) 15:08 - Implementation and Tensor Shapes 17:30 - Conclusion and External Resources